ارزیابی کیفیت محیط زیستی با استفاده از ابزار سنجش از دور و شبکه های عصبی مصنوعی (مطالعه موردی: تبریز- رشت)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد/ گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز.

2 دانش آموخته کارشناسی ارشد مهندسی منابع آب/گروه مهندسی آب، دانشکده عمران، دانشگاه تبریز، تبریز.

3 استادیار / گروه مهندسی آب، دانشکده مهندسی عمران، دانشگاه تبریز، تبریز.

چکیده

در مقاله حاضر، جهت ارزیابی کیفیت زیست‌محیطی برای 500 پیکسل در اطراف تبریز در استان آذربایجان‌شرقی و همچنین 500 پیکسل در اطراف رشت در استان گیلان در ایران که از لحاظ اقلیم با یکدیگر متفاوت می‌باشند، با استفاده از محاسبات‌نرم و سنجش از دور، اندیس زیست‌محیطی EBV(Eco-environment Background Value) ، جهت تعیین کیفیت زیست‌محیطی مناطق، مورد بررسی قرار‌گرفته‌است. برای مدل‌سازی، از شاخص‌های پوشش‌گیاهی، رطوبت‌خاک، درخشندگی، دمای‌سطح‌زمین و داده‌های رقومی ارتفاعی که با استفاده از ابزار ‌سنجش از دور تهیه شد و همچنین از داده‌های مربوط به بارش و دما به عنوان ورودی‌ به مدل شبکه عصبی مصنوعی back propagation سه لایه، بهره‌گیری شده‌است. میانگین داده‌های مربوط به 8‌سال گذشته برای شاخص‌های مذکور، یک‌بار به صورت فصلی برای چهار فصل و بار دیگر به‌صورت سالانه برای مناطق مورد بررسی در اطراف تبریز و رشت وارد شبکه شدند. نتیجه حاصل، نشان‌گر عملکرد بهتر شبکه برای منطقه تبریز در فصل بهار با RMSE=0.0219 و R=0.9961 می‌باشد. به نظر می‌رسد دلیل عملکرد بهتر شبکه برای تبریز در مقایسه با رشت را می‌توان ضعف ابزار سنجش از دور در بررسی مکان‌هایی همچون گیلان دانست که پوشش گیاهی متراکم و رطوبت جوی بالایی دارند. به‌نظر می‌رسد تراکم پوشش-گیاهی و رطوبت بالا مانع از بازتاب مناسب و بدون انحراف از سطح زمین می‌شود و در دریافت داده‌های مورد نیاز، اخلال ایجاد می‌کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Eco-environmental Quality Evaluation Using Remote Sensing and Artificial Neural Network (Case Study: Tabriz-Rasht)

نویسندگان [English]

  • Vahid Nourani 1
  • Ehsan Foroumandi 2
  • Elnaz Sharghi 3
1 Prof., Department of Water Resources Eng., Faculty of Civil Engineering, University of Tabriz, Iran.
2 M.Sc. Graduate, Department of Water Resources Eng., Faculty of Civil Engineering, University of Tabriz, Iran.
3 Assistant Prof., Department of Water Resources Eng., Faculty of Civil Engineering, University of Tabriz, Iran.
چکیده [English]

In this study, to evaluate the eco-environment value of 500 pixels around the city of Tabriz in the East Azarbaijan Province, Iran, as well as 500 pixels around the city of Rasht in Gilan Province, Iran, which have different climates, the Eco-environment Background Value index (EBV) has been investigated using soft computations and remote sensing tools to determine the eco-environment value of the areas. For modeling, indicators including vegetation index, soil wetness index, Land Surface Temperature (LST), and Digital Elevation Model (DEM) data collected using remote sensing tools as well as data on precipitation and temperature obtained using ground-based weather stations were exploited as input into the three-layer back propagation based artificial neural network (BPANN) model. The average of the data for the past 8 years for these indicators, once seasonally for four seasons and once annually for the regions under study around Tabriz and Rasht, entered the network. The results indicated a better performance of the network for Tabriz region in the spring with root mean square error (RMSE) = 0.0219 and R = 0.9961. It seems that the better network performance for Tabriz compared to Rasht could be due to the weakness of the remote sensing tool in examining areas like Gilan, which has a dense vegetation and high atmospheric humidity. It seems that the high vegetation density and high humidity impede proper reflection without deviation from the land surface and disrupts the reception of the required data.

کلیدواژه‌ها [English]

  • Eco-environment value assessment
  • Remote Sensing
  • Meteorological data
  • Artificial Neural Network
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